A VLP-based vaccine targeting domain III of the West Nile virus E protein protects from lethal infection in mice

Background. Since its first appearance in the USA in 1999, West Nile virus (WNV) has spread in the Western hemisphere and continues to represent an important public health concern. In the absence of effective treatment, there is a medical need for the development of a safe and efficient vaccine. Live attenuated WNV vaccines have shown promise in preclinical and clinical studies but might carry inherent risks due to the possibility of reversion to more virulent forms. Subunit vaccines based on the large envelope (E) glycoprotein of WNV have therefore been explored as an alternative approach. Although these vaccines were shown to protect from disease in animal models, multiple injections and/or strong adjuvants were required to reach efficacy, underscoring the need for more immunogenic, yet safe DIII-based vaccines. Results. We produced a conjugate vaccine against WNV consisting of recombinantly expressed domain III (DIII) of the E glycoprotein chemically cross-linked to virus-like particles derived from the recently discovered bacteriophage AP205. In contrast to isolated DIII protein, which required three administrations to induce detectable antibody titers in mice, high titers of DIII-specific antibodies were induced after a single injection of the conjugate vaccine. These antibodies were able to neutralize the virus in vitro and provided partial protection from a challenge with a lethal dose of WNV. Three injections of the vaccine induced high titers of virus-neutralizing antibodies, and completely protected mice from WNV infection. Conclusions. The immunogenicity of DIII can be strongly enhanced by conjugation to virus-like particles of the bacteriophage AP205. The superior immunogenicity of the conjugate vaccine with respect to other DIII-based subunit vaccines, its anticipated favourable safety profile and low production costs highlight its potential as an efficacious and cost-effective prophylaxis against WNV

Novel genetic variant in FTO influences insulin levels and insulin resistance in severely obese children and adolescents

Background: The global prevalence of obesity and overweight is increasing rapidly among adults as well as among children and adolescents. Recent genome-wide association studies have provided strong support for association between variants in the FTO gene and obesity. We sequenced regions of the FTO gene to identify novel variants that are associated with obesity and related metabolic traits. Results: We screened exons 3 and 4 including exon-intron boundaries in FTO in 48 obese children and adolescents and identified three novel single nucleotide polymorphism in the fourth intronic region, (c.896 + 37A > G, c.896 + 117C > G and c.896 + 223A > G). We further genotyped c.896 + 223A > G in 962 subjects, 450 well-characterized obese children andadolescents and 512 adolescents with normal weight. Evidence for differences in genotype frequencies were not detected for the c.896 + 223A > G variant between extremely obese children and adolescents and normal weight adolescents (P = 0.406, OR = 1.154 (0.768-1.736)). Obese subjects with the GG genotype, however, had 30% increased fasting serum insulin levels (P = 0.017) and increased degree of insulin resistance (P = 0.025). There were in addition no differences in body mass index (BMI) or BMI standard deviation score (SDS) levels among the obese subjects according to genotype and the associations with insulin levels and insulin resistance remained significant when adjusting for BMI SDS. Conclusion: These findings suggest that this novel variant in FTO is affecting metabolic phenotypes such as insulin resistance, which are not mediated through differences in BMI levels

Partitioning heritability of regulatory and cell-type-specific variants across 11 common diseases

Regulatory and coding variants are known to be enriched with associations identified by genome-wide association studies (GWASs) of complex disease, but their contributions to trait heritability are currently unknown. We applied variance-component methods to imputed genotype data for 11 common diseases to partition the heritability explained by genotyped SNPs (hg(2)) across functional categories (while accounting for shared variance due to linkage disequilibrium). Extensive simulations showed that in contrast to current estimates from GWAS summary statistics, the variance-component approach partitions heritability accurately under a wide range of complex-disease architectures. Across the 11 diseases DNaseI hypersensitivity sites (DHSs) from 217 cell types spanned 16% of imputed SNPs (and 24% of genotyped SNPs) but explained an average of 79% (SE = 8%) of hg(2) from imputed SNPs (5.1 7 enrichment; p = 3.7 7 10(-17)) and 38% (SE =4%) of hg(2) from genotyped SNPs (1.6 7 enrichment, p = 1.0 7 10(-4)). Further enrichment was observed at enhancer DHSs and cell-type-specific DHSs. In contrast, coding variants, which span 1% of the genome, explained <10% of hg(2) despite having the highest enrichment. We replicated these findings but found no significant contribution from rare coding variants in independent schizophrenia cohorts genotyped on GWAS and exome chips. Our results highlight the value of analyzing components of heritability to unravel the functional architecture of common disease

Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Polygenic risk scores have shown great promise in predicting complex disease risk and will become more accurate as training sample sizes increase. The standard approach for calculating risk scores involves linkage disequilibrium (LD)-based marker pruning and applying a p\ua0value threshold to association statistics, but this discards information and can reduce predictive accuracy. We introduce LDpred, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel. Theory and simulations show that LDpred outperforms the approach of pruning followed by thresholding, particularly at large sample sizes. Accordingly, predicted R(2) increased from 20.1% to 25.3% in a large schizophrenia dataset and from 9.8% to 12.0% in a large multiple sclerosis dataset. A similar relative improvement in accuracy was observed for three additional large disease datasets and for non-European schizophrenia samples. The advantage of LDpred over existing methods will grow as sample sizes increase

LD Score regression distinguishes confounding from polygenicity in genome-wide association studies

Both polygenicity (many small genetic effects) and confounding biases, such as cryptic relatedness and population stratification, can yield an inflated distribution of test statistics in genome-wide association studies (GWAS). However, current methods cannot distinguish between inflation from a true polygenic signal and bias. We have developed an approach, LD Score regression, that quantifies the contribution of each by examining the relationship between test statistics and linkage disequilibrium (LD). The LD Score regression intercept can be used to estimate a more powerful and accurate correction factor than genomic control. We find strong evidence that polygenicity accounts for the majority of the inflation in test statistics in many GWAS of large sample size